The introduction of innovative digital tools for supporting manufacturing processes has far-reaching effects at an organizational and individual level due to the development of Industry 4.0. The FACTS4WORKERS project funded by H2020, i.e., Worker-Centric Workplaces in Smart Factories, aims to develop user-centered assistance systems in order to demonstrate their impact and applicability at the shop floor. To achieve this, understanding how to develop such tools is as important as assessing if advantages can be derived from the ICT system created. This study introduces the technology of a workplace solution linked to the industrial challenge of self-learning manufacturing workplaces. Subsequently, a two-step approach to evaluate the presented system is discussed, consisting of the one used in FACTS4WORKERS and the one used in the “Heuristics for Industry 4.0” project. Both approaches and the use case are introduced as a base for presenting the comparison of the results collected in this paper. The comparison of the results for the presented use case is extended with the results for the rest of the FACTS4WORKERS use cases and with future work in the framework.
It is well-known that the introduction of innovative digital tools in manufacturing due to Industry 4.0 has far-reaching effects on an organizational and on an individual level. The H2020 funded project FACTS4WORKERS-Worker-Centric Workplaces in Smart Factories-aims to develop user-centered assistance systems in order to demonstrate their impact and applicability at the shop floor. To do so it is important to understand how to develop such tools and how to assess if advantages can be derived from the created ICT system. This study introduces the technology of a workplace solution that is linked to a specific industrial challenge. Subsequently, a 2-stepped approach to evaluate the presented system is discussed. Heuristics, which are an output of project "Heuristics for Industry 4.0" are used to test if the developed solution covers critical aspects of socio-technical system design. Insights into the design,
Machine Learning (ML) techniques can be used to convert Big Data into valuable information for agri-environmental applications, such as predictive pest modeling. Lobesia botrana (Denis & Schiffermüller) 1775 (Lepidoptera: Tortricidae) is one of the main pests of grapevine, causing high productivity losses in some vineyards worldwide. This work focuses on the optimization of the Touzeau model, a classical correlation model between temperature and L. botrana development using data-driven models. Data collected from field observations were combined with 30 GB of registered weather data updated every 30 min to train the ML models and make predictions on this pest’s flights, as well as to assess the accuracy of both Touzeau and ML models. The results obtained highlight a much higher F1 score of the ML models in comparison with the Touzeau model. The best-performing model was an artificial neural network of four layers, which considered several variables together and not only the temperature, taking advantage of the ability of ML models to find relationships in nonlinear systems. Despite the room for improvement of artificial intelligence-based models, the process and results presented herein highlight the benefits of ML applied to agricultural pest management strategies.
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